import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[Any]			,       UpperCamelCase     :				List[str]=()			,       UpperCamelCase     :				Optional[Any]=None			,       UpperCamelCase     :				Union[str, Any]="no"			,       UpperCamelCase     :				str="29500"							)   ->    List[Any]:
		_lowerCamelCase				   =  False
		_lowerCamelCase				   =  False
		if any(key.startswith('KAGGLE'							) for key in os.environ.keys()							):
				_lowerCamelCase				   =  True
		elif "IPython" in sys.modules:
				_lowerCamelCase				   =  'google.colab' in str(sys.modules['IPython'].get_ipython()							)

		try:
				_lowerCamelCase				   =  PrecisionType(mixed_precision.lower()							)
		except ValueError:
				raise ValueError(
				    F"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""							)

		if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'			,       UpperCamelCase							) is not None):
				# TPU launch
				import torch_xla.distributed.xla_multiprocessing as xmp

				if len(AcceleratorState._shared_state							) > 0:
						raise ValueError(
						    'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
						    'your training function. Restart your notebook and make sure no cells initializes an '
						    '`Accelerator`.'							)
				if num_processes is None:
						_lowerCamelCase				   =  8

				_lowerCamelCase				   =  PrepareForLaunch(UpperCamelCase			,       distributed_type='TPU'							)
				print(F"""Launching a training on {num_processes} TPU cores."""							)
				xmp.spawn(UpperCamelCase			,       args=UpperCamelCase			,       nprocs=UpperCamelCase			,       start_method='fork'							)
		elif in_colab:
				# No need for a distributed launch otherwise as it's either CPU or one GPU.
				if torch.cuda.is_available():
						print('Launching training on one GPU.'							)
				else:
						print('Launching training on one CPU.'							)
				function(*UpperCamelCase							)
		else:
				if num_processes is None:
						raise ValueError(
						    'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'							)

				if num_processes > 1:
						# Multi-GPU launch
						from torch.multiprocessing import start_processes
						from torch.multiprocessing.spawn import ProcessRaisedException

						if len(AcceleratorState._shared_state							) > 0:
								raise ValueError(
								    'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
								    'inside your training function. Restart your notebook and make sure no cells initializes an '
								    '`Accelerator`.'							)

						if torch.cuda.is_initialized():
								raise ValueError(
								    'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
								    'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
								    'function.'							)

						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=UpperCamelCase			,       master_addr='127.0.01'			,       master_port=UpperCamelCase			,       mixed_precision=UpperCamelCase							):
								_lowerCamelCase				   =  PrepareForLaunch(UpperCamelCase			,       distributed_type='MULTI_GPU'							)
								print(F"""Launching training on {num_processes} GPUs."""							)
								try:
										start_processes(UpperCamelCase			,       args=UpperCamelCase			,       nprocs=UpperCamelCase			,       start_method='fork'							)
								except ProcessRaisedException as e:
										if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
												raise RuntimeError(
												    'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
												    'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
												    'Please review your imports and test them when running the `notebook_launcher()` to identify '
												    'which one is problematic.'							) from e

				else:
						# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
						if is_mps_available():
								_lowerCamelCase				   =  '1'
								print('Launching training on MPS.'							)
						elif torch.cuda.is_available():
								print('Launching training on one GPU.'							)
						else:
								print('Launching training on CPU.'							)
						function(*UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Optional[Any]=()			,       UpperCamelCase     :				Dict=2							)   ->    Optional[int]:
		from torch.multiprocessing import start_processes

		with tempfile.NamedTemporaryFile() as tmp_file:
				# torch.distributed will expect a few environment variable to be here. We set the ones common to each
				# process here (the other ones will be set be the launcher).
				with patch_environment(
				    world_size=UpperCamelCase			,       master_addr='127.0.01'			,       master_port='29500'			,       accelerate_mixed_precision='no'			,       accelerate_debug_rdv_file=tmp_file.name			,       accelerate_use_cpu='yes'			,       ):
						_lowerCamelCase				   =  PrepareForLaunch(UpperCamelCase			,       debug=UpperCamelCase							)
						start_processes(UpperCamelCase			,       args=UpperCamelCase			,       nprocs=UpperCamelCase			,       start_method='fork'							)
from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()